A Comprehensive Systematic Review of TinyML for Person Detection Systems.
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| Title: | A Comprehensive Systematic Review of TinyML for Person Detection Systems. |
|---|---|
| Authors: | Soliman, Yehia A.1 yahia.abuelkhair@fci.helwan.edu.eg, Ghoneim, Amr S.2 amr.ghoneim@fci.helwan.edu.eg, Elkhouly, Mahmoud M.3 elkhouly@fci.helwan.edu.eg |
| Source: | IAENG International Journal of Computer Science. Nov2025, Vol. 52 Issue 11, p4074-4086. 13p. |
| Subjects: | Machine learning, Computing platforms, Benchmark problems (Computer science), Intelligent sensors, Automatic tracking, Mathematical optimization, Artificial neural networks |
| Abstract: | Tiny Machine Learning (TinyML) enables the deployment of machine learning models on ultra-low-power and memory-constrained edge devices. This capability is crucial for person detection systems in applications such as smart homes, wearable health monitors, industrial safety, and wildlife surveillance. However, deploying person detection on microcontrollers poses significant challenges due to limited computation, memory, and energy resources. This paper presents a systematic literature review (SLR) of recent research in TinyML-based person detection from 2014 to 2024. We explore lightweight neural network architectures (e. g., MobileNet, Tiny-YOLO), optimization techniques (e. g., quantization, pruning, knowledge distillation), and performance metrics, including accuracy, latency, and energy efficiency. We also assess the suitability of edge hardware platforms such as ARM Cortex-M, ESP32, STM32, Jetson Nano, and Raspberry Pi. The review identifies current trends, highlights practical constraints, and proposes future directions involving adaptive models, federated learning, and privacypreserving designs. This work serves as a reference for researchers and practitioners aiming to build efficient, scalable, and real-time TinyML-based person detection systems. [ABSTRACT FROM AUTHOR] |
| Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 189071834 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Comprehensive Systematic Review of TinyML for Person Detection Systems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Soliman%2C+Yehia+A%2E%22">Soliman, Yehia A.</searchLink><relatesTo>1</relatesTo><i> yahia.abuelkhair@fci.helwan.edu.eg</i><br /><searchLink fieldCode="AR" term="%22Ghoneim%2C+Amr+S%2E%22">Ghoneim, Amr S.</searchLink><relatesTo>2</relatesTo><i> amr.ghoneim@fci.helwan.edu.eg</i><br /><searchLink fieldCode="AR" term="%22Elkhouly%2C+Mahmoud+M%2E%22">Elkhouly, Mahmoud M.</searchLink><relatesTo>3</relatesTo><i> elkhouly@fci.helwan.edu.eg</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. Nov2025, Vol. 52 Issue 11, p4074-4086. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computing+platforms%22">Computing platforms</searchLink><br /><searchLink fieldCode="DE" term="%22Benchmark+problems+%28Computer+science%29%22">Benchmark problems (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+sensors%22">Intelligent sensors</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+tracking%22">Automatic tracking</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Tiny Machine Learning (TinyML) enables the deployment of machine learning models on ultra-low-power and memory-constrained edge devices. This capability is crucial for person detection systems in applications such as smart homes, wearable health monitors, industrial safety, and wildlife surveillance. However, deploying person detection on microcontrollers poses significant challenges due to limited computation, memory, and energy resources. This paper presents a systematic literature review (SLR) of recent research in TinyML-based person detection from 2014 to 2024. We explore lightweight neural network architectures (e. g., MobileNet, Tiny-YOLO), optimization techniques (e. g., quantization, pruning, knowledge distillation), and performance metrics, including accuracy, latency, and energy efficiency. We also assess the suitability of edge hardware platforms such as ARM Cortex-M, ESP32, STM32, Jetson Nano, and Raspberry Pi. The review identifies current trends, highlights practical constraints, and proposes future directions involving adaptive models, federated learning, and privacypreserving designs. This work serves as a reference for researchers and practitioners aiming to build efficient, scalable, and real-time TinyML-based person detection systems. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 4074 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Computing platforms Type: general – SubjectFull: Benchmark problems (Computer science) Type: general – SubjectFull: Intelligent sensors Type: general – SubjectFull: Automatic tracking Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Artificial neural networks Type: general Titles: – TitleFull: A Comprehensive Systematic Review of TinyML for Person Detection Systems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Soliman, Yehia A. – PersonEntity: Name: NameFull: Ghoneim, Amr S. – PersonEntity: Name: NameFull: Elkhouly, Mahmoud M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 1819656X Numbering: – Type: volume Value: 52 – Type: issue Value: 11 Titles: – TitleFull: IAENG International Journal of Computer Science Type: main |
| ResultId | 1 |
